In the radiographic diagnostic process, anatomical motion blur is a frequently cited reason for image rejection, due to the loss of image detail and sharpness induced by motion. There are two common sources of anatomical motion blur in medical radiographs. One source is patient movement during the image capture, that is, external motion. The result of external motion is blurred appearance of an entire anatomical region in the image. A second source relates to internal motion due to the normal involuntary functioning of anatomy. For example, the beating of the heart can cause some amount of blur either directly, if the heart tissue lies within the image, or indirectly, by the compensating movement of surrounding structures. This effect can result in blur within local regions of a chest radiograph.
Motion blur due to camera shaking and inadvertent motion of the subject has been a recognized problem in photography. There have been a number of solutions proposed for reducing image blur in photographs.
U.S. Pat. No. 7,181,082 B2 (Feng), entitled “Blur Detection System” describes a system of estimating and reducing image blur using a sequence of discrete cosine transform (DCT) coefficients arranged in a plurality of blocks on digital images.
U.S. Patent Application No. US 2005/0231603 (Poon), entitled “Motion Blur Correction” discloses a method of correcting blur in a motion image by estimating the direction and extent of blur based on edge response of the motion-blurred image over a set of directions, computing an error image between the actual motion-blurred image and a “guess image” generated by the estimated blur function, and then finally correcting the image blur using the error image.
U.S. Pat. No. 6,987,530 B2 (McConica), entitled “Method for Reducing Motion Blur in a Digital Image” relates to a method for detecting and reducing motion blur that includes calculation of first and second figures of merit associated with two substantially orthogonal directions and comparison of the figures of merit and adjustment of magnitude of the amplitude of spatial frequencies of the image in response to the comparison.
While these references relate to systems/methods to compensate for motion blur for digital photography, such systems/methods are not appropriate for diagnostic imaging. There are significant differences between photographic and diagnostic images, and differences in how the sensing apparatus responds to motion during image capture. The sources of blur motion themselves also differ significantly between photographic and diagnostic imaging systems. In photographic images, motion blur typically originates from external sources, such as object movement, camera shake or focus errors. Blur compensation (such as using, for example, a de-blur kernel) may be desirable for photographic images; however, this type of solution can be undesirable for diagnostic images, as it may unintentionally alter image contents and introduce artifacts or mask problems. For such reasons, conventional approaches to image blur that might be useful in photographic imaging can have little or no relevance for diagnostic imaging.
Accordingly, there exists a need for a method that detects and reports anatomical motion blur resulting from either external or internal motion in radiographic medical image data.